This paper presents the results of floral spectral studies on 1275 flowers from India, Brazil, Israel, Germany, and Norway. Floral spectral reflectance from 400 to 700 nm (nm) was used to ...quantitatively represent ‘human-perceived’ color of flowers in Red, Green, Blue color space. Floral spectral reflectance from 350 to 600 nm was used to discern and objectively represent ‘insect pollinator-perceived’ flower colors in color hexagon. We leverage the advantage offered by ‘quantified human perception’ provided by ‘human-perceived’ floral colors to represent the distribution of floral hues and uncover the relationship between the composition of incoming solar radiation and predominant ‘human-perceived’ floral colors at the tropics and the higher latitudes. Further, the observed species-level mutual exclusivity of ‘insect pollinator-perceived’ floral colors is stated as chromatic exclusivity hypothesis. We compare ‘human-perceived’ and ‘insect pollinator-perceived’ floral colors at Trivandrum (India) and provide a physical explanation for short and long ‘wavelength triads’ of insect pollinator and human visual sensitivity respectively.
•Objective representation of human and insect pollinator perceived floral colors.•Reports the mutual exclusivity of insect pollinator-perceived floral colors.•The composition of the incoming solar spectrum determines the floral colors.•Floral radiometry will complement the development of national pollinator strategies.
Vegetation indices are widely used to assess quantitatively the biophysical characteristics of vegetation from remote sensing measurements. Different indices have their own advantages in retrieving ...vegetation information. It is very difficult to precisely attribute any vegetation index to any particular vegetation biophysical parameter. This study examines the correlations among different vegetation indices derived from a set of mustard, gram and wheat fields at three different phenological growth stages. The results are presented as correlation matrices along with correlation scatter plots. Homologous (equi-magnitude) vegetation information is represented by NDVI, PVI and AtRVI for wheat crop with leaf area index less than 1.
Foliar colour is the resultant of physical interaction of light on structureswithin and on surface of leaves. Quantitative representation of foliar colour provides a means to express the condition of ...crop.The Red, Green Blue (RGB) values from raw digital photographs of rice crops were transformed to CIE L a b colour space in Matlab®. Authors present results that convey strong relation between colour dimension 'a' of Commission Internationale de l'Eclairage (CIE) L a b colour model and Green Red Vegetation Index (GRVI). More than 90% variability of Green Red Vegetation Index (GRVI) of rice crop was explained by 'a'. This provides an affordable means for ground based remote observation to monitor crop condition in real-time.
Noncontact biometrics such as face and iris have additional benefits over contact-based biometrics such as fingerprint and hand geometry. However, three important challenges need to be addressed in a ...noncontact biometrics-based authentication system: ability to handle unconstrained acquisition, robust and accurate matching, and privacy enhancement without compromising security. In this paper, we propose a unified framework based on random projections and sparse representations, that can simultaneously address all three issues mentioned above in relation to iris biometrics. Our proposed quality measure can handle segmentation errors and a wide variety of possible artifacts during iris acquisition. We demonstrate how the proposed approach can be easily extended to handle alignment variations and recognition from iris videos, resulting in a robust and accurate system. The proposed approach includes enhancements to privacy and security by providing ways to create cancelable iris templates. Results on public data sets show significant benefits of the proposed approach.
While recent techniques for discriminative dictionary learning have demonstrated tremendous success in image analysis applications, their performance is often limited by the amount of labeled data ...available for training. Even though labeling images is difficult, it is relatively easy to collect unlabeled images either by querying the web or from public datasets. In this paper, we propose a discriminative dictionary learning technique which utilizes both labeled and unlabeled data for learning dictionaries. Extensive evaluation on existing datasets demonstrate that the proposed method performs significantly better than state of the art dictionary learning approaches when unlabeled images are available for training.
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